Hamidreza Mahyar Homepage

“The discipline of good time management spreads to all your other disciplines.”   <Brian Tracy>
Hamidreza Mahyar :: حمیدرضا ماهیار


Hamidreza Mahyar

 

Ph.D. Candidate

Performance and Dependability Laboratory (PDL),

Department of Computer Engineering,

Sharif University of Technology (SUT).



E-mail      hmahyar (at) ce (dot) sharif (dot) edu

Phone      (+98) 21 6616 6678

Address   PDL, Room 815, CE Department,
                Sharif University of Technology, Tehran.


Homepage at Google scholar

 


Short Biography

Hamidreza Mahyar received his B.Sc. degree in Computer Engineering in 2009. From 2009 to 2011, he was an M.Sc. student of CE Department at Science and Research University. Hamidreza is currently a Ph.D. candidate with the Department of Computer Engineering at Sharif University of Technology (SUT), since 2011, under supervision of Prof. Ali Movaghar and Prof. Hamid R. Rabiee. His research interests lie in the area of Social Network Analysis, Network Centrality, Sparse Recovery in Networks, Compressive Sensing, Network Tomography, Study of Partially-Observed Complex Networks.

 

Membership and Honors

  • Student Member of IEEE.

  • IEEE Signal Processing Society Membership.

  • IEEE Communications Society Membership.

  • IEEE Young Professionals.

  • IEEE Sensors Council

  • IEEE Systems Council

  • Informatics Society of Iran Membership.

  • IT Member, Alumni Association of Faculty of Engineering, University of Tehran.

  • Certificate of "Modern Management in the 21st century", Brian Tracy International.

 

Research Interests
 

  • Social Network Analysis
  • Network Centrality
  • Sparse Recovery in Networks
  • Compressive Sensing
  • Network Tomography
  • Study of Partially-Observed Complex Networks

Abstract: The Identifying nodes with high centrality (i.e. betweenness or closeness) values has many applications in real-world systems. In mobile social networks, high betweenness nodes should be updated promptly for security reasons. In unstructured peer-to-peer networks, these nodes can slow down the performance of the entire network and thus provisioning them well may improve performance. Moreover, such nodes see a big part of the traffic so they can be used for network monitoring. There exist polynomial-time algorithms to compute the exact centrality values, but they are not practical for the analysis of the very large networks that are of interest these days. In practice, users are interested in the relative ranking of the vertices according to their centrality, rather than the actual value of the centrality, so a very good estimation of the value of each node is sufficiently informative for most purposes. It is therefore natural to develop algorithms that trade off accuracy for speed and efficiently compute high-quality approximations of the centrality values. Nevertheless, in order for these algorithms to be practical, they must scale well and have a low runtime dependency on the size of the network. Fast centrality estimation, as a good approximation, is thus an important problem and it would be an acceptable alternative to exact scores. It is noteworthy that in many cases we are only interested in the highest centrality nodes rather than the centralities of all the nodes in a network. This is reasonable since a node with a higher centrality is viewed as a more important node than a node with a lower centrality, such as finding influencers in a social network, and locating bottlenecked junctions/routers in a transportation network/the Internet. In addition, a community detection application, one of the most prominent application of the centrality, utilizes the highest centrality nodes only. Despite the above evident applications, identification of nodes with the highest centrality problem is hardly discussed in the literature. Node centrality is relevant to problems such as identifying important nodes that control flows of information between separate parts of the network and identifying causal nodes to influence other entities behavior, such as genes in genomics or customers in marketing studies. Furthermore, Identifying nodes with high centrality values has been utilized to analyze social networks and protein networks, to identify significant nodes in wireless ad hoc networks, to study the importance and activity of nodes in mobile phone call networks and interaction patterns of players on massively multiplayer online games, to study online expertise sharing communities such as physicians, to identify and analyze linking behavior of key bloggers in dynamic networks of blog posts and to measure network traffic in communication networks. We want to propose an efficient algorithm to ensure that the centrality of all (or top) nodes in a social network is well approximated. We tend to provide a general framework to identify nodes with high betweenness centrality and closeness centrality.

 

 

 

Publications

 



 

Teaching and Research Experiences

Spring 2016: Instructor, Principles of Computer System Design, Department of Mathematical and Computer Sciences, Sharif University of Technology, Tehran, Iran.

Fall 2015: Instructor, Data Communication and Computer Networks, Department of Mathematical and Computer Sciences, Sharif University of Technology, Tehran, Iran.

Spring 2015: Instructor, Principles of Computer System Design, Department of Mathematical and Computer Sciences, Sharif University of Technology, Tehran, Iran.

Spring 2015: Instructor, Operating System, Department of Mathematical and Computer Sciences, Sharif University of Technology, Tehran, Iran.

Spring 2015: Instructor, Data Structure and Algorithm, Department of Computer Engineering, Sharif University of Technology, Tehran International Campus, Tehran, Iran.

Spring 2015: Instructor, English for IT Engineering, Department of Computer Engineering, Sharif University of Technology, Tehran International Campus, Tehran, Iran.

Fall 2014: Instructor, Data Communication and Computer Networks, Department of Mathematical and Computer Sciences, Sharif University of Technology, Tehran, Iran.

Fall 2014: Instructor, English for IT Engineering, Department of Computer Engineering, Sharif University of Technology, Tehran International Campus, Tehran, Iran.

Spring 2014: Instructor, Data Structure and Algorithm, Department of Computer Engineering, Sharif University of Technology, Tehran International Campus, Tehran, Iran.

Spring 2014: Instructor, Operating System, Department of Mathematical and Computer Sciences, Sharif University of Technology, Tehran, Iran.

Spring 2014: Instructor, English for IT Engineering, Department of Computer Engineering, Sharif University of Technology, Tehran International Campus, Tehran, Iran.

Nov. 2013 - Present: Research Assistant, Performance and Dependability Laboratory (PDL), Department of Computer Engineering, Sharif University of Technology, Tehran, Iran.

Fall 2013: Instructor, English for IT Engineering, Department of Computer Engineering, Sharif University of Technology, Tehran, Iran.

Fall 2012-Spring 2013-Fall 2013: Instructor, Computer Networks Lab., Department of Computer Engineering, Sharif University of Technology, Tehran, Iran.

Spring 2013: Teaching Assistant, Multimedia Networks, Department of Computer Engineering, Sharif University of Technology, Tehran, Iran.

Oct. 2011 - Nov. 2013: Research Assistant, Mobile Value Added Services Laboratory (VASLAB), Department of Computer Engineering, Sharif University of Technology, Tehran, Iran.

Oct. 2011 - Nov.2013: Research Assistant, Digital Media Laboratory (DML), Department of Computer Engineering, Sharif University of Technology, Tehran, Iran.

Fall 2011: Instructor, Computer Networks Security, Department of Computer Engineering, University of Applied Science, Tehran, Iran.

Fall 2011: Instructor, Network Management Operating Systems, Department of Computer Engineering, University of Applied Science, Tehran, Iran.

Spring 2008: Instructor, Electronic Commerce (e-Commerce), Department of Computer Engineering, University of Applied Science, Tehran, Iran.

Spring 2008: Instructor, Principles of Web Design (HTML), Department of Computer Engineering, University of Applied Science, Tehran, Iran.

Spring 2008: Instructor, English for Computer Engineering, Department of Computer Engineering, University of Applied Science, Tehran, Iran.

Summer 2007 - Spring 2008: Instructor, Information Storage and Retrieval, Department of Computer Engineering, Management and Technology Institute of Amirkabir, Tehran, Iran.

Contact Information


Performance and Dependability Laboratory (PDL)
 

Room 815, Department of Computer Engineering,
Sharif University of Technology, P.O. Box 11155-9517, Azadi Ave., Tehran, Iran.


Phone:   (+98) 21 6616 6678
E-mail    hmahyar@ce.sharif.edu

Our Location

Sharif University of Technology